Methods, Internet of Things systems, and mediums for correcting smart gas flow

ABSTRACT

The present disclosure provides methods, Internet of Things (IoT) systems, and mediums for correcting a smart gas flow. The method may be implemented by a smart gas device management platform of an IoT system for correcting a smart gas flow. The method may include: obtaining reading data of a gas meter; determining a first confidence level of the reading data based on the reading data; in response to a determination that the first confidence level is smaller than a confidence level threshold, obtaining a working condition parameter; and determining, based on the working condition parameter, a gas meter correction mode.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority of Chinese Patent Application No.202310095073.2, filed on Feb. 10, 2023, the contents of which areentirely incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the field of a gas device correction,and in particular, to methods, Internet of Things (IoT) systems, andmediums for correcting a smart gas flow.

BACKGROUND

When the gas meter is measuring a gas flow, errors may occur due to useof the gas meter under a non-standard temperature or pressure condition,so that a reading of the gas meter cannot reflect a real volume or flowof gas. Therefore, it is desirable to provide a method for correcting agas flow, which can realize an intelligent temperature and pressurecompensation for the reading of the gas meter or an intelligentreplacement of the gas meter.

SUMMARY

One or more embodiments of the present disclosure provide a method forcorrecting a smart gas flow. The method may be implemented by a smartgas device management platform of an Internet of Things (IoT) system forcorrecting a smart gas flow. The method may include: obtaining readingdata of a gas meter; determining a first confidence level of the readingdata based on the reading data; in response to a determination that thefirst confidence level is smaller than a confidence level threshold,obtaining a working condition parameter; and determining, based on theworking condition parameter, a gas meter correction mode.

One or more embodiments of the present disclosure provide an Internet ofThings (IoT) system for correcting a smart gas flow. The IoT system mayinclude a smart gas user platform, a smart gas service platform, a smartgas device management platform, a smart gas sensor network platform, anda smart gas object platform. The smart gas device management platformmay be configured to: obtain reading data of a gas meter; determine afirst confidence level of the reading data based on the reading data; inresponse to a determination that the first confidence level is smallerthan a confidence level threshold, obtaining a working conditionparameter; and determine, based on the working condition parameter, agas meter correction mode.

One or more embodiments of the present disclosure provide anon-transitory computer-readable storage medium storing computerinstructions. When reading the computer instructions in the storagemedium, a computer may implement the method for correcting a smart gasflow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further illustrated in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures, wherein:

FIG. 1 is a schematic diagram illustrating an exemplary applicationscenario of an Internet of Things (IoT) system for correcting a smartgas flow according to some embodiments of the present disclosure;

FIG. 2 is a block diagram illustrating an exemplary IoT system forcorrecting a smart gas flow according to some embodiments of the presentdisclosure;

FIG. 3 is a flowchart illustrating an exemplary process of a method forcorrecting a smart gas flow according to some embodiments of the presentdisclosure;

FIG. 4 is a flowchart illustrating an exemplary process for determininga first confidence level according to some embodiments of the presentdisclosure;

FIG. 5 is a flowchart illustrating an exemplary process for determininga first confidence level according to other embodiments of the presentdisclosure;

FIG. 6 is a structural diagram illustrating an exemplary confidencelevel determination model according to some embodiments of the presentdisclosure; and

FIG. 7 is a structural diagram illustrating an exemplary reading datacorrection model according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In order to more clearly illustrate the technical solutions related tothe embodiments of the present disclosure, a brief introduction of thedrawings referred to the description of the embodiments is providedbelow. Obviously, the drawings described below are only some examples orembodiments of the present disclosure. Those having ordinary skills inthe art, without further creative efforts, may apply the presentdisclosure to other similar scenarios according to these drawings.Unless obviously obtained from the context or the context illustratesotherwise, the same numeral in the drawings refers to the same structureor operation.

It should be understood that the “system,” “device,” “unit,” and/or“module” used herein are one method to distinguish different components,elements, parts, sections, or assemblies of different levels. However,if other words can achieve the same purpose, the words can be replacedby other expressions.

As used in the disclosure and the appended claims, the singular forms“a,” “an,” and “the” include plural referents unless the content clearlydictates otherwise; the plural forms may be intended to include singularforms as well. In general, the terms “comprise,” “comprises,” and/or“comprising,” “include,” “includes,” and/or “including,” merely promptto include steps and elements that have been clearly identified, andthese steps and elements do not constitute an exclusive listing. Themethods or devices may also include other steps or elements.

The flowcharts used in the present disclosure illustrate operations thatthe system implements according to the embodiment of the presentdisclosure. It should be understood that the foregoing or followingoperations may not necessarily be performed exactly in order. Instead,the operations may be processed in reverse order or simultaneously.Besides, one or more other operations may be added to these processes,or one or more operations may be removed from these processes.

FIG. 1 is a schematic diagram illustrating an exemplary applicationscenario of an Internet of Things (IoT) system for correcting a smartgas flow according to some embodiments of the present disclosure.

As shown in FIG. 1 , an application scenario 100 may include a server110, a network 120, a terminal device 130, a monitoring device 140, astorage device 150, and a gas meter 160.

In some embodiments, the application scenario 100 may determine a smartgas flow correction solution by implementing a method and/or the IoTsystem for correcting a smart gas flow disclosed in the presentdisclosure. For example, in a typical application scenario, the IoTsystem for correcting a smart gas flow may obtain reading data of thegas meter through a third-party platform or the gas meter 160. Theserver 110 (a processing device) may determine a first confidence levelof the reading data based on the reading data; in response to adetermination that the first confidence level is smaller than aconfidence level threshold, obtain a working condition parameter; anddetermine, based on the working condition parameter, a gas metercorrection mode. For more description about the above process, pleaserefer to FIG. 3 and related description thereof.

The server 110 may be connected with the terminal device 130 through thenetwork 120, and the server 110 may be connected with the storage device150 through the network 120. The server 110 may include a processingdevice, and the processing device may be configured to execute themethod for correcting the smart gas flow described in some embodimentsof the present disclosure. The network 120 may connect variouscomponents of the application scenario 100 and/or connect the system andexternal resource parts. The storage device 150 may be configured tostore data and/or instructions, for example, the storage device 150 maystore the reading data of the gas meter, the first confidence level, theworking condition parameter, and the gas meter correction mode, etc. Thestorage device 150 may be directly connected to the server 110 or may bearranged inside the server 110. The terminal device 130 refers to one ormore terminal devices or software. In some embodiments, the terminaldevice 130 may receive information related to the gas meter correctionmanner sent by the processing device, and display the information to auser. In some embodiments, the terminal device 130 may be configured toinput confirmation information related to the method for correcting thesmart gas flow by the user, and send the confirmation information to theserver 110. Exemplarily, the terminal device 130 may include a mobiledevice 130-1, a tablet computer 130-2, a laptop computer 130-3, otherdevices with input and/or output functions, or any combination thereof.The monitoring device 140 may be configured to obtain the workingcondition parameter. An exemplary monitoring device 140 may include atemperature sensor 140-1, a pressure sensor 140-2, a camera, a harmfulgas sensing device, etc. In some scenarios, the application scenario ofthe IoT system for correcting a smart gas flow may not include themonitoring device 140, and may directly obtain the working conditionparameter from the third-party platform. The gas meter 160 may beconfigured to obtain the reading data. The reading data may includecurrent reading data and historical reading data. The gas meter may be adevice configured to meter the gas flow. For example, the gas meter mayinclude a turbine gas flow meter, a diaphragm gas meter, a roots gasflow meter, etc. In some embodiments, the gas meter 160 may send theobtained reading data to other assemblies (such as the server 110, theterminal device 130, or the storage device 150, etc.) through thenetwork 120.

It should be noted that the application scenario 100 is provided for thepurpose of illustration, and not intended to limit the scope of thepresent disclosure. For those skilled in the art, various modificationsor changes may be made based on the description of the presentdisclosure. For example, the application scenario 100 may furtherinclude a database. As another example, the application scenario 100 maybe implemented on other devices to achieve similar or differentfunctions. However, these changes and modifications do not depart fromthe scope of the present disclosure.

The IoT system may be an information processing system that includessome or all of a user platform, a service platform, a managementplatform, a sensor network platform, and an object platform. The userplatform may be a functional platform configured to obtain userperceptual information and generates control information. The serviceplatform may be configured to connect the management platform and theuser platform, and play functions of perceptual information servicecommunication and control information service communication. Themanagement platform may be configured to overall plan and coordinateconnection and cooperation between various functional platforms (e.g.,the user platform and the service platform). The management platform maybe configured to gather the information of the IoT operation system andmay provide functions of perception management and control managementfor the IoT operation system. The sensor network platform may beconfigured to connect the management platform and the object platform,and play functions of perceptual information sensor communication andcontrol information sensor communication. The object platform may be afunctional platform configured to generate perceptual information.

The processing of information in the IoT system may be divided into aprocessing process of the user perceptual information and a processingprocess of the control information. The control information may beinformation generated based on the user perceptual information. In someembodiments, the control information may include user demand controlinformation, and the user perceptual information may include user queryinformation. The processing of the perceptual information may be thatthe object platform obtains the perceptual information and transmits theperceptual information to the management platform through the sensornetwork platform. The user demand control information may be transmittedby the management platform to the user platform through the serviceplatform, thereby realizing control of sending prompt information.

FIG. 2 is a block diagram illustrating an exemplary IoT system forcorrecting a smart gas flow according to some embodiments of the presentdisclosure.

As shown in FIG. 2 , the IoT system 200 for correcting a smart gas flowmay include a smart gas user platform 210, a smart gas service platform220, a smart gas device management platform 230, a smart gas sensornetwork platform 240, and a smart gas object platform 250. In someembodiments, the IoT system 200 for correcting a smart gas flow may be apart of a server or may be implemented by the server.

In some embodiments, the IoT system 200 for correcting a smart gas flowmay be applied to various scenarios of a terminal management. In someembodiments, the IoT system 200 for correcting a smart gas flow mayobtain reading data of a gas meter; determine a first confidence levelof the reading data based on the reading data; in response to adetermination that the first confidence level is smaller than aconfidence level threshold, obtain a working condition parameter; anddetermine, based on the working condition parameter, a gas metercorrection mode.

Various scenarios of the IoT system 200 for correcting a smart gas flowmay include a gas user use scenario, a government user use scenario, asupervision user use scenario, etc. It should be noted that the abovescenarios are only examples, and do not limit the specific applicationscenarios of the IoT system 200 for correcting a smart gas flow. Thoseskilled in the art may apply the IoT system 200 for correcting a smartgas flow to any other suitable scenarios on the basis of thedescriptions disclosed in the embodiment.

The smart gas user platform 210 may be a user-oriented platform thatobtains a user demand and feeds back information to the user. In someembodiments, the smart gas user platform 210 may interact with the user.In some embodiments, the smart gas user platform 210 may be configuredas a terminal device, for example, a smart device such as a mobilephone, a computer, etc.

In some embodiments, the smart gas user platform 210 may include a gasuser sub-platform, a government user sub-platform, and a supervisionuser sub-platform. The gas user may receive information related to gasmeter reading data and gas meter correction manner sent by the smart gasservice platform 220 through the gas user sub-platform. The gas user mayfurther interact with the smart gas service platform 220 to sendconfirmation information related to a smart gas flow correctionsolution. The government user may obtain a gas operation service of thesmart gas service platform 220 through the government user sub-platform.The supervision user may send a query instruction or a controlinstruction for the gas meter reading data and the gas meter correctionmanner to the smart gas service platform 220 through the supervisionuser sub-platform, and obtain the gas meter reading data, the gas metercorrection mode, the working condition parameter, etc. The gas user maybe a user of a gas device. The government user may be a governmentmanager related to an activity such as a gas facility protection, a gassafety accident prevention and handling, a gas operation management,etc. The supervision user may be a manager or a government officer whoperforms a safety monitoring on the gas device and the gas meteringsystem. In some embodiments, the smart gas user platform 210 may obtainthe instruction input by the user through the terminal device, and queryinformation related to the gas meter reading data and the gas metercorrection mode. In some embodiments, the smart gas user platform 210may obtain the user's confirmation information related to the gas meterreading data and the gas meter correction manner through the terminaldevice.

The smart gas service platform 220 may be a platform that providesinformation/data transmission and interaction.

In some embodiments, the smart gas service platform 220 may beconfigured for the information and/or data interaction between the smartgas device management platform 230 and the smart gas user platform 210.For example, the smart gas service platform 220 may receive the queryinstruction sent by the smart gas user platform 210 for storage, andthen send the query instruction to the smart gas device managementplatform 230, and further obtain the information related to the gasmeter reading data and the gas meter correction manner from the smartgas device management platform 230 for storage, and then send theinformation to the smart gas device management platform 230. As anotherexample, the smart gas service platform 220 may send the gas meterreading data and the gas meter correction manner to the smart gas userplatform 210, and obtain the confirmation information related to the gasmeter reading data and the gas meter correction manner from the userplatform 210 for storage, and then send the information to the smart gasdevice management platform 230.

In some embodiments, the smart gas service platform 220 may include asmart gas use service sub-platform, a smart operation servicesub-platform, and a smart supervision service sub-platform. In someembodiments, the smart gas service sub-platform may be configured toreceive the information related to the gas meter reading data and thegas meter correction manner sent by the smart gas device managementplatform 230, and send the information to the gas user platform. In someembodiments, the smart supervision service sub-platform may beconfigured to receive the query instruction sent by the government usersub-platform, and send the query instruction to the smart gas devicemanagement platform 230. In some embodiments, the smart supervisionservice sub-platform may be configured to receive the controlinstruction sent by the supervision user sub-platform, and send thecontrol instruction to the smart gas device management platform 230.

The smart gas device management platform 230 may refer to an IoTplatform configured to overall plan and coordinate the connection andcooperation between various functional platforms, and provide functionsof perception management and control management.

In some embodiments, the smart gas device management platform 230 may beconfigured for information and/or data processing. For example, thesmart gas device management platform 230 may be configured formonitoring and warning of the device working condition parameter andremote management of device parameter.

In some embodiments, the smart gas device management platform 230 may befurther configured for information and/or data interaction between thesmart gas service platform 220 and the smart gas sensor network platform240. For example, the smart gas device management platform 230 mayreceive the query instruction sent by the smart gas service platform 220(such as the smart supervision service sub-platform) for storage, andthen send the query instruction to the smart gas sensor network platform240, and may obtain information related to an open or closed state of asmart gas terminal from the smart gas sensor network platform 240 forstorage, and then send the information to the smart gas service platform220. As another example, the smart gas device management platform 230may send the information related to the gas meter reading data and thegas meter correction manner to the smart gas service platform 220 (suchas the smart gas use service sub-platform), obtain the conformationinformation related to the gas meter reading data and the gas metercorrection mode, process the conformation information, and then send theconformation information to the smart gas sensor network platform 240.

In some embodiments, the smart gas device management platform 230 mayinclude a smart gas indoor device parameter management sub-platform, asmart gas pipeline network device parameter management sub-platform, anda smart gas data center.

The smart gas indoor device parameter management sub-platform may beconfigured for the remote management and parameter monitoring andwarning of a smart gas indoor device. In some embodiments, the smart gasindoor device parameter management sub-platform may include a deviceoperation parameter monitoring and warning module and a device parameterremote management module.

The smart gas pipeline network device parameter management sub-platformmay be configured for remote management and parameter monitoring andwarning of a smart gas pipeline network device. In some embodiments, thesmart gas pipeline network device parameter management sub-platform mayinclude a device operation parameter monitoring and warning module and adevice parameter remote management module.

The smart gas data center may be a data management sub-platform forstoring, calling, and transferring data. The smart gas data center maystore historical data, for example, historical reading data, ahistorical gas meter correction mode, etc. The above data may beobtained through manual input or historical execution of the method. Insome embodiments, the smart gas data center may be configured to sendthe gas meter reading data and the gas meter correction manner to thesmart gas service platform 220.

In some embodiments, the smart gas device management platform 230 may beconfigured to obtain the reading data of the gas meter; determine afirst confidence level of the reading data based on the reading data; inresponse to a determination that the first confidence level is smallerthan a confidence level threshold, obtain a working condition parameter;and determine, based on the working condition parameter, a gas metercorrection mode.

In some embodiments, the smart gas device management platform 230 may befurther configured to: predict, based on the first reading data, adistribution interval of third reading data and a distributionprobability corresponding to the third reading data; and take adistribution probability corresponding to the second reading data in thedistribution interval of the third reading data as the first confidencelevel.

In some embodiments, the smart gas device management platform 230 may befurther configured to: determine, based on the first reading datasequence, a historical flow distribution condition; determine, based onthe second reading data sequence, a current flow distribution condition;determine, based on a difference between the historical flowdistribution condition and the current flow distribution condition, thefirst confidence level.

In some embodiments, the smart gas device management platform 230 may befurther configured to: determine, based on the historical flowdistribution condition and the current flow distribution condition, thefirst confidence level through a confidence level determination model.The confidence level determination model may be a machine learningmodel.

In some embodiments, the smart gas device management platform 230 may befurther configured to: determine, based on the second reading data, astandard temperature and pressure, and a current temperature andpressure, a correction value of the second reading data through areading data correction model. The reading data correction model may bea machine learning model.

For further descriptions on the smart gas device management platform230, please refer to FIG. 3 , FIG. 4 , FIG. 5 , FIG. 6 , FIG. 7 , andthe related description thereof.

The smart gas sensor network platform 240 may refer to a platform forunified management of sensor communication between platforms of the IoTsystem 200. In some embodiments, the smart gas sensor network platform240 may be configured as a communication network and a gateway. In someembodiments, the smart gas sensor network platform 240 may include asmart gas indoor device sensor network sub-platform and a smart gaspipeline network device sensor network sub-platform. The smart gassensor network platform 240 may adopt a plurality of groups of gatewayservers or a plurality of groups of intelligent routers, which are notlimited here.

In some embodiments, the smart gas sensor network platform 240 may beconfigured for sensor communication of the smart gas indoor device andthe smart gas pipeline network device. In some embodiments, the smartgas sensor network platform 240 may be configured to send the readingdata to the smart gas data center. In some embodiments, the smart gassensor network platform 240 may be configured to send the method forcorrecting a smart gas flow of the smart gas data center to the smartgas object platform 250.

The smart gas object platform 250 may be a functional device using gas.In some embodiments, the smart gas object platform 250 may be configuredas a smart gas terminal, such as a gas use device, an intelligent gasmeter, etc. The smart gas object platform 250 may obtain the readingdata of the gas meter. In some embodiments, the smart gas objectplatform 250 may be configured as a monitoring device, such as atemperature sensor, a pressure sensor, a camera, a harmful gasmonitoring device, etc. The smart gas object platform 250 may obtain theworking condition parameter. In some embodiments, the smart gas objectplatform 250 may send the working condition parameter to the smart gasdevice management platform 230 through the smart gas sensor networkplatform 240. In some embodiments, the smart gas object platform 250 mayinclude a smart gas indoor device object sub-platform and a smart gaspipeline network device object sub-platform. The smart gas indoor deviceobject sub-platform may be configured as various gas terminals, such asa gas stove, a gas water heater, a gas meter, etc. The smart gaspipeline network device object sub-platform may be configured as a gaspipeline network pressure regulating device, etc.

In some embodiments of the present disclosure, through theabove-mentioned IoT system, opposition between different types of datamay be guaranteed, so as to ensure classified transmission, traceabilityof the data and classified issuance and processing of instructions,which can make the structure of the IoT and data processing clear andcontrollable, and facilitate the management, control and data processingof the IoT.

FIG. 3 is a flowchart illustrating an exemplary process of a method forcorrecting a smart gas flow according to some embodiments of the presentdisclosure. In some embodiments, the process 300 may be executed by asmart gas device management platform of an IoT system for correcting asmart gas flow. As shown in FIG. 3 , the process 300 may include thefollowing operations.

In 310, obtaining reading data of a gas meter.

In some embodiments of the present disclosure, the reading data may be agas flow count display value of the gas meter. The reading data mayreflect information such as a gas flow, gas consumption, etc. Forexample, the reading data may be 168 m³, indicating that a volume of gaspassing through the gas meter (i.e., the volume of gas that has beenused) is 168 m³ since the gas meter starts metering.

In some embodiments, the smart gas device management platform may obtainat least one reading data through a smart gas object platform (such as agas meter). The reading data may include current reading data andhistorical reading data. The current reading data may be the gasconsumption from the time when the gas meter starts metering to acurrent time point. The historical reading data may be the gasconsumption from the time when the gas meter starts metering to acertain historical settlement point. It may be understood that the gasmeter is settled at an interval (such as a month), and a differencebetween the current reading data and the historical reading data may betaken as the gas consumption of a time period from the historicalsettlement point to the current time point.

In some embodiments, the historical reading data may be the gasconsumption from the time when gas meter starts metering to other timepoints. For example, the historical reading data may be the gasconsumption per hour since the gas meter starts metering.

In 320, determining a first confidence level of the reading data basedon the reading data.

The first confidence level may indicate credibility of the reading data.As an actual working environment of the gas meter may affect themetering of the gas meter, the reading data of the gas meter may deviatefrom an actual gas flow. Therefore, the first confidence level may betaken as a parameter to measure accuracy of the reading data.

The first confidence level may be a specific numerical value within 10or 100. For example, the first confidence level may be 90. The greaterthe first confidence level is, the closer the reading data of the gasmeter may be to the actual gas flow (or gas consumption). The smallerthe first confidence level is, the greater a difference between thereading data of the gas meter and the actual gas flow (or gasconsumption) may be.

In some embodiments, the first confidence level may be obtained byperforming a statistical analysis on a historical confidence levelcorresponding to the historical reading data. For example, the smart gasdevice management platform may determine the first confidence levelthrough a point estimation manner based on the historical confidencelevel corresponding to the historical reading data. For furtherdescription regarding the above process, please refer to FIG. 4 andrelated description thereof.

In some embodiments, the first confidence level may be determined basedon a difference between a historical flow distribution condition and acurrent flow distribution condition. For further description regardingdetermining the first confidence level based on the difference betweenthe historical flow distribution condition and the current flowdistribution condition, please refer to FIG. 5 and the relateddescriptions thereof.

In some embodiments, the first confidence level may be determinedthrough an artificial intelligence model. For example, the smart gasdevice management platform may determine the first confidence levelthrough a confidence level determination model. For specific descriptionregarding the confidence level determination model, please refer to FIG.6 and the related descriptions thereof.

In 330, in response to a determination that the first confidence levelis smaller than a confidence level threshold, obtaining a workingcondition parameter.

The confidence level threshold may be a confidence level critical valuefor determining whether the reading data corresponding to the firstconfidence level needs to be corrected. The confidence level thresholdmay be determined empirically. For example, the confidence levelthreshold may be 90. When the first confidence level is smaller than 90,the smart gas device management platform may obtain the workingcondition parameter for a subsequent operation.

The working condition parameter may refer to a parameter involved in areal-time environment where the gas meter works. For example, theworking condition parameter may include a temperature of the workingenvironment of the gas meter, a gas pressure to which the gas meter issubjected, etc. In some embodiments, the working condition parameter maybe determined by a device related to the gas meter. For example, thecorresponding working condition parameter may be obtained through thetemperature sensor 140-1, the pressure sensor 140-2, the camera, theharmful gas sensing device, etc. in the monitoring device 140.

In 340, determining, based on the working condition parameter, a gasmeter correction manner.

The gas meter correction manner may refer to a measure taken on the gasmeter to make up for the deviation of the reading data. For example, thegas meter correction manner may include reminding to replace the gasmeter, using a spare gas meter, performing a correction calculation onthe reading data of the gas meter, etc. An exemplary correctioncalculation may include a temperature compensation calculation, apressure compensation calculation, etc.

In some embodiments, after the user replaces a new gas meter, the smartgas device management platform may continue to execute the method on thenew gas meter. When the first confidence level corresponding to thereading data of the new gas meter is still smaller than the confidencelevel threshold, the smart gas device management platform may remind asupervision user to provide a door-to-door inspection for the gas meter.

In some embodiments, for a certain gas pipeline network, when thedifference between the working condition parameter corresponding to acertain gas meter in the gas pipeline network and the working conditionparameters corresponding to other gas meters in the gas pipeline networkis greater than a difference threshold, the smart gas device managementplatform may determine the gas meter as a gas meter with suspectedleakage, and may remind the supervision user to provide a door-to-doorinspection for the gas meter. The difference threshold may be a manuallypreset value.

Through the method for correcting a smart gas flow described in someembodiments of the present disclosure, the accuracy of the reading dataof the gas meter may be determined, and the gas meter with a relativelygreat deviation may be corrected. The entire determination process doesnot require a manual participation, thereby reducing an effect ofsubjective factors. In addition, the determination process is carriedout based on the current reading data, the historical reading data andthe working condition parameter of the gas meter, which can improve thecorrelation between the determination process and actual work of the gasmeter.

FIG. 4 is a flowchart illustrating an exemplary process for determininga first confidence level according to some embodiments of the presentdisclosure. In some embodiments, the process 400 may be executed by asmart gas device management platform of an IoT system for correcting asmart gas flow. As shown in FIG. 4 , the process 400 may include thefollowing operations.

In 410, predicting, based on the first reading data, a distributioninterval of third reading data and a distribution probabilitycorresponding to the third reading data.

In some embodiments, the reading data may include first reading data andsecond reading data. The first reading data may be historical readingdata of a gas meter. For example, the first reading data may be thereading data of the gas meter at a certain historical settlement point.For example, the reading data in January, 2010 is 25.56 m³, and thereading data in February, 2010 is 47.88 m³. In some embodiments, thefirst reading data may be determined by the gas meter, or may bedetermined by calling from a storage device, a network, etc. The secondreading data may be the reading data of the gas meter at a current timepoint. For example, the second reading data may be a current displayvalue of the gas meter of 51.22 m³.

The third reading data may be a theoretical value of the current readingdata. In some embodiments, the distribution interval of the thirdreading data and the first confidence level corresponding to the thirdreading data may be calculated by a statistical manner. For example,when a difference between the first reading data corresponding toadjacent historical settlement points subjects to a normal distributionwith a mathematical expectation of p and a standard deviation of σ, thethird reading data may be calculated through the following process.

In S1, calculating, based on the first reading data corresponding toeach historical settlement point, the difference between the firstreading data corresponding to the adjacent historical settlement points;

In S2, calculating, based on each difference and the first confidencelevel corresponding to each difference, the mathematical expectation μand the standard deviation a to obtain the normal distribution N (μ,σ²);

In S3, standardizing the normal distribution N (μ, σ²);

In S4, checking, based on the current time point, a standard normaldistribution table to obtain the third reading data and the confidencelevel corresponding to the third reading data.

Exemplarily, assuming that the current time point is February, 2020, thetheoretical value of the current reading data may be calculated asfollows.

In S1, adjacently subtracting the reading data of January, 2010(confidence level of 90), the reading data of February, 2010 (confidencelevel of 92) . . . the reading data of January, 2020 (confidence levelof 91) obtained by the gas meter to obtain a plurality of differencevalues (i.e., gas flow per month). Each difference may correspond to aconfidence level (generally, the confidence level of the next month maybe taken as the confidence level of the difference). For example, thedifference of January, 2010 (i.e., the gas flow of the month) may beobtained by subtracting the reading data of January, 2010 from thereading data of February, 2010, and the confidence level correspondingto the reading data of February, 2010 may be taken as the confidencelevel of the difference.

In S2, calculating the mathematical expectation p and the standarddeviation a based on each difference and confidence level correspondingto each difference to obtain the normal distribution N (μ, σ²).

In S3, standardizing the normal distribution N (μ, σ²) to obtain astandard normal distribution.

In S4, checking the standard normal distribution table to obtain thereading data corresponding to February, 2020 and the theoretical valueof the confidence level corresponding to the reading data.

It should be noted that the difference between the first reading datacorresponding to the adjacent historical settlement points subjects tothe normal distribution only for illustration, and other statisticallaws may also exist, such as a Poisson distribution, a geometricdistribution, a binomial distribution, etc. The above statistical lawsmay be used to calculate the first confidence level, which is notrepeated in the present disclosure.

In 420, taking a distribution probability corresponding to the secondreading data in the distribution interval of the third reading data asthe first confidence level. For example, when the second reading data is40.20 m³, the distribution probability corresponding to 40.20 m³ in thethird reading data may be taken as the first confidence level of thesecond reading data.

Through the method described in the embodiment, it is possible to find astatistical law from the historical reading data, and predict the firstconfidence level, so that the predicted first confidence level can becloser to the actual historical data.

FIG. 5 is a flowchart illustrating an exemplary process for determininga first confidence level according to other embodiments of the presentdisclosure. In some embodiments, the process 500 may be executed by asmart gas device management platform of an IoT system for correcting asmart gas flow. As shown in FIG. 5 , the process 500 may include thefollowing operations.

In 510, determining, based on a first reading data sequence, ahistorical flow distribution condition.

In some embodiments, the reading data may include the first reading datasequence and a second reading data sequence. The first reading datasequence may be a data set composed of a plurality of first readingdata. For example, for each hour in January, 2010, the correspondingfirst reading data sequence may be (20.00 m³, 20.12 m³, 20.45 m³, . . ., 25.56 m³). The above first reading data may be the reading data of thegas meter per hour in January, 2010.

In some embodiments, the smart gas device management platform may beconfigured to fit the first reading data sequence to obtain atime-reading data function p(x), and take the function p(x) as thehistorical flow distribution condition. An exemplary fitting manner maybe to list the first reading data sequence as a point set in atime-reading data coordinate system, and connect the points with asmooth curve. An ordinate of the time-reading data function p(x) may bethe reading data, and an abscissa may be the time point. An exemplarytime-reading data function p(x) may be an exponential function, a powerfunction, etc.

In 520, determining, based on the second reading data sequence, acurrent flow distribution condition.

The second reading data sequence may be a data set composed of aplurality of second reading data. For example, for each hour of thecurrent day, the corresponding second reading data sequence may be(40.05 m³, 40.14 m³, 40.22 m³, . . . , 40.25 m³). The above secondreading data may be the reading data of the gas meter per hour of theday of the current time.

In some embodiments, the smart gas device management platform may beconfigured to fit the second reading data sequence to obtain atime-reading data function q(x), and take the function q(x) as thecurrent flow distribution condition. An exemplary fitting manner may beto list the second reading data sequence as a point set in atime-reading data coordinate system, and connect the points with asmooth curve. An ordinate of the time-reading data function q(x) may bethe reading data, and an abscissa may be the time point. An exemplarytime-reading data function q(x) may be an exponential function, a powerfunction, etc.

In 530, determining, based on a difference between the historical flowdistribution condition and the current flow distribution condition, thefirst confidence level.

It may be understood that, for a same user, the difference between thehistorical flow distribution condition and the current flow distributioncondition may be smaller than a flow difference threshold. The flowdifference threshold may be a preset value. For example, there is nogreat fluctuation in a count of family members, a count of gas devices,and a gas use duration of the same user. If the above difference isgreater than the flow difference threshold, which may indicate that thefirst reading data sequence and the second reading data sequence do notmeet a gas use law of the same user, the reading data may correspond toa relatively small first confidence level. If the above difference issmaller than the flow difference threshold, which may indicate that thefirst reading data sequence and the second reading data sequence meetthe gas use law of the same user, the reading data may correspond to arelatively great first confidence level. The relatively small firstconfidence level may be a preset value, such as 0. The relatively greatfirst confidence level may be a preset value, such as 90.

In some embodiments, the first confidence level may be determined bycalculating a Kullback-Leibler divergence (KL divergence) of thetime-reading data functions p(x) and q(x). An exemplary calculationequation is as follows.

${M = \frac{1}{1 + e^{KL}}},$where M denotes the first confidence level; KL denotes the KL divergenceof p(x) and q(x).

In some embodiments, the smart gas device management platform may beconfigured to compare small flow data and large flow data of thehistorical flow distribution condition with small flow data and largeflow data of the current flow distribution condition to determine thefirst confidence level.

For example, the reading data with flow increase less than 0.01 m³ perhour may be regarded as the small flow data, and the reading data withflow increase more than 1.5 m³ per hour may be regarded as the largeflow data. Assuming that a ratio of the small flow data of thehistorical flow distribution condition to the small flow data of thecurrent flow distribution condition is A, and a ratio of the large flowdata of the historical flow distribution condition to the large flowdata of the current flow distribution condition is B. By checking apreset relationship table between the A, B and the first confidencelevel, the first confidence level may be determined. The presetrelationship table between the A, B and the first confidence level mayinclude each ratio of A to B (such as 0.9, 1.0, 1.5, etc.) and thecorresponding first confidence levels (such as 90, 100, 60, etc.). Itmay be understood that the ratio of A to B is closer to 1, A is closerto B, and the first confidence level is greater. The relationship tablebetween the A, B and the first confidence level may be determinedempirically.

In the embodiments of the present disclosure, by analyzing the curvefitted by the historical reading data and the current reading data, thefirst confidence level conforming to the gas use law of the user may beobtained, so that the predicted first confidence level can be moreaccurate.

FIG. 6 is a structural diagram illustrating an exemplary confidencelevel determination model according to some embodiments of the presentdisclosure.

In some embodiments, a smart gas device management platform maydetermine a first confidence level through a confidence degreedetermination model based on a historical flow distribution conditionand a current flow distribution condition. The confidence leveldetermination model may be a machine learning model, for example, aneural network model. As shown in FIG. 6 , an input of the confidencelevel determination model 630 may include the historical flowdistribution condition 610 and the current flow distribution condition620. An output of the confidence level determination model 630 mayinclude the first confidence level 640. For the historical flowdistribution condition 610 and the current flow distribution condition620, please refer to FIG. 5 and the related description thereof.

In some embodiments, the confidence level determination model 630 may beobtained by training a great number of training samples with labels.Specifically, a plurality of groups of training samples with labels maybe input into an initial confidence level determination model, a lossfunction may be constructed based on an output of the initial confidencelevel determination model and the labels, and parameters of theconfidence level determination model may be iteratively updated throughtraining based on the loss function. In some embodiments, the trainingmay be performed by various modes based on the training samples. Forexample, the training may be performed based on a gradient descent. Whena preset condition is met, the training may end and a trained confidencelevel determination model may be obtained. The preset condition may bethat the loss function converges.

In some embodiments, the training sample may include the historical flowdistribution condition and a randomly generated reading to be predicted.The label may be a first confidence level corresponding to the randomlygenerated reading to be predicted. The training sample may be determinedby calling information stored in a storage device. The label may beobtained by manual labeling. In some embodiments, the training samplemay further include the historical flow distribution condition andcorrected actual reading data of the gas meter.

In some embodiments, the confidence level determination model 630 mayfurther include a correction layer 631. An input of the correction layer631 may include the first confidence level 640, a reference historicalflow distribution condition 670, and the current flow distributioncondition 620. An output of the correction layer 631 may include asecond confidence level 680. For the first confidence level 640, pleaserefer to FIG. 3 and the related description thereof. For the currentflow distribution condition 620, please refer to FIG. 5 and the relateddescription thereof.

The reference historical flow distribution condition may refer to afirst reading data sequence of a gas meter of a reference user. Thereference user may be determined through manual selection. In someembodiments, the reference user may be a user whose normal distributionrelationship is similar to the current user. For example, when adifference between the first reading data corresponding to the adjacenthistorical settlement points of the current user subjects to the normaldistribution with a mathematical expectation of μ and a standarddeviation of σ, and the first reading data corresponding to the adjacenthistorical settlement points of a certain user subjects to or is similarto the above-mentioned normal distribution, the user may be regarded asthe reference user. The reference historical flow distribution conditionmay be a historical reading data sequence under a standard condition(such as a standard working condition parameter condition), or ahistorical reading data sequence corrected by the gas meter.

The second confidence level may be a corrected first confidence level.

In some embodiments, the correction layer 631 may be obtained bytraining a great number of training samples with labels. Specifically, aplurality of groups of training samples with labels may be input into aninitial correction layer, a loss function may be constructed based on anoutput of the initial correction layer and the labels, and parameters ofthe correction layer may be iteratively updated through training basedon the loss function.

In some embodiments, the training may be performed by various modesbased on the training samples. For example, the training may beperformed based on a gradient descent. When a preset condition is met,the training may end and a trained correction layer may be obtained. Thepreset condition may be that the loss function converges.

In some embodiments, the training sample may include a historical firstconfidence level, the historical flow distribution condition and thereference historical flow distribution condition. The label may be ahistorical second confidence level (that is, the historically correctedfirst confidence level). The training sample may be determined bycalling the information stored in the storage device. The label may beobtained by manual labeling.

In some embodiments, the input of the correction layer 631 may furtherinclude a standard temperature and pressure 650 and a currenttemperature and pressure 660. The standard temperature and pressure maybe a recommended temperature and pressure used by the gas meter. Thestandard temperature and pressure may be a specific temperature rangeand/or pressure range. In some embodiments, the standard temperature andpressure may be determined through factory setting of the gas meter. Thecurrent temperature and pressure may be the temperature and pressure ofan actual working environment of the gas meter. In some embodiments, thecurrent temperature and pressure may be determined by the workingcondition parameter. The greater a difference between the standardtemperature and pressure and the current temperature and pressure is,the worse the working condition of the gas meter may be, and the lowerthe confidence level of the gas meter reading data may be. For example,when the current temperature is −20° C., an error may occur to the gasmeter due to moisture freezing in the gas.

In the embodiment of the present disclosure, by introducing reading dataof other users with similar gas use habits into the artificialintelligence model, as well as the temperature and pressure parameter ofthe actual working condition of the gas meter, a count of impact factorsof the confidence level may be increased. After being corrected by thecorrection layer, the first confidence level that is more in line withthe actual condition can be obtained.

FIG. 7 is a structural diagram illustrating an exemplary reading datacorrection model according to some embodiments of the presentdisclosure.

In some embodiments, a working condition parameter may include astandard temperature and pressure and a current temperature andpressure. In some embodiments, the smart gas device management platformmay determine, based on second reading data, the standard temperatureand pressure, and the current temperature and pressure, a correctionvalue of second reading data through the reading data correction model.The reading data correction model may be a machine learning model, forexample, a neural network model. As shown in FIG. 7 , an input of thereading data correction model 740 may include the second reading data710, the standard temperature and pressure 720 and the currenttemperature and pressure 730. An output of the reading data correctionmodel 740 may include a correction value 750 of the second reading data.For the second reading data 710, please refer to FIG. 4 and the relateddescription thereof. For the standard temperature and pressure 720 andthe current temperature and pressure 730, please refer to FIG. 6 and therelated description thereof.

In some embodiments, the reading data correction model 740 may beobtained by training a great number of training samples with labels.Specifically, a plurality of groups of training samples with labels maybe input to an initial reading data correction model, a loss functionmay be constructed based on an output of the initial reading datacorrection model and the labels, and parameters of the reading datacorrection model may be iteratively updated through training based onthe loss function.

In some embodiments, the training may be performed by various modesbased on the training samples. For example, the training may beperformed based on a gradient descent. When a preset condition is met,the training may end and a trained reading data correction model may beobtained. The preset condition may be that the loss function converges.

In some embodiments, the training sample may include historical readingdata, the standard temperature and pressure, and a historicaltemperature and pressure. The label may be a correction value of thehistorical reading data. The training sample may be determined bycalling information stored in a storage device. The label may beobtained by manual labeling.

In some embodiments, the input of the reading data correction model 740may further include a gas composition 760 and a gas calorific value 770.Wear and corrosion of the gas meter caused by gas may affect the readingdata of the gas meter. For example, when the gas contains acid gas, adegree of corrosion of the gas meter caused by the gas may increase,which may accordingly increase an error of the reading data of the gasmeter and reduce the first confidence level. In some embodiments, thegas composition and the gas calorific value may be determined by a gassupplier. The gas composition and the gas calorific value may be used asthe input, which may take into account an impact of the gas on thereading data of the gas meter, thereby improving accuracy of the outputreading data.

In some embodiments, the smart gas device management platform maydetermine the confidence level corresponding to the output correctionvalue of the second reading data. For specific description ondetermining the confidence level, please refer to FIG. 4 , FIG. 5 andFIG. 6 and the specific description thereof. In some embodiments, whenthe confidence level corresponding to the correction value of the secondreading data is greater than a confidence level threshold, the smart gasdevice management platform may determine the confidence level as thefirst confidence level, and determine the correction value of the secondreading data as a final second reading data.

In some embodiments of the present disclosure, the current reading dataof the gas meter may be corrected through the actual working conditionparameter of the gas meter and the information of the gas, so as toimprove a matching degree between the predicted reading data and theactual gas flow, and obtain reading data that is more in line with theactual condition.

The present disclosure provides a non-transitory computer-readablestorage medium storing computer instructions. When reading the computerinstructions in the storage medium, a computer may implement the methodfor correcting a smart gas flow.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Although not explicitly stated here,those skilled in the art may make various modifications, improvementsand amendments to the present disclosure. These alterations,improvements, and modifications are intended to be suggested by thisdisclosure, and are within the spirit and scope of the exemplaryembodiments of this disclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various parts of this specification are not necessarilyall referring to the same embodiment. In addition, some features,structures, or features in the present disclosure of one or moreembodiments may be appropriately combined.

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. However, thisdisclosure does not mean that the present disclosure object requiresmore features than the features mentioned in the claims. Rather, claimedsubject matter may lie in less than all features of a single foregoingdisclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the present disclosureare to be understood as being modified in some instances by the term“about,” “approximate,” or “substantially.” For example, “about,”“approximate,” or “substantially” may indicate ±20% variation of thevalue it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the present disclosure are approximations, thenumerical values set forth in the specific examples are reported asprecisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of the presentdisclosure disclosed herein are illustrative of the principles of theembodiments of the present disclosure. Other modifications that may beemployed may be within the scope of the present disclosure. Thus, by wayof example, but not of limitation, alternative configurations of theembodiments of the present disclosure may be utilized in accordance withthe teachings herein. Accordingly, embodiments of the present disclosureare not limited to that precisely as shown and described.

What is claimed is:
 1. A method for correcting a smart gas flow,implemented by a smart gas device management platform of an Internet ofThings (I) system for correcting a smart gas flow, comprising: obtainingreading data of a gas meter; determining a first confidence level of thereading data based on the reading data; wherein the reading dataincludes first reading data and second reading data, the first readingdata being historical reading data of the gas meter, the second readingdata being current reading data corresponding to a current time point,and the determining a first confidence level of the reading data basedon the reading data comprising: predicting, based on the first readingdata, a distribution interval of third reading data and a distributionprobability corresponding to the third reading data; the third readingdata being a theoretical value of the current reading data, and thecurrent reading data being a gas consumption from time when the gasmeter starts metering to the current time point; and taking adistribution probability corresponding to the second reading data in thedistribution interval of the third reading data as the first confidencelevel; in response to a determination that the first confidence level issmaller than a confidence level threshold, obtaining a working conditionparameter; the confidence level threshold being determined through anexperience value; and determining, based on the working conditionparameter, a gas meter correction manner; the working conditionparameter including a standard temperature and pressure and a currenttemperature and pressure, and the determining, based on the workingcondition parameter, a gas meter correction manner comprising:determining, based on the second reading data, the standard temperatureand pressure, and the current temperature and pressure, a correctionvalue of the second reading data through a reading data correctionmodel, wherein the reading data correction model is a machine learningmodel.
 2. The method of claim 1, wherein the reading data includes afirst reading data sequence and a second reading data sequence, and thedetermining a first confidence level of the reading data based on thereading data comprises: determining, based on the first reading datasequence, a historical flow distribution condition; determining, basedon the second reading data sequence, a current flow distributioncondition; and determining, based on a difference between the historicalflow distribution condition and the current flow distribution condition,the first confidence level.
 3. The method of claim 1, wherein thedetermining a first confidence level of the reading data based on thereading data comprises: determining, based on the historical flowdistribution condition and the current flow distribution condition, thefirst confidence level through a confidence level determination model,wherein the confidence level determination model is a machine learningmodel.
 4. The method of claim 3, wherein the confidence leveldetermination model further includes a correction layer, and an input ofthe correction layer includes the first confidence level, a referencehistorical flow distribution condition, and the current flowdistribution condition, and an output of the correction layer includes asecond confidence level.
 5. The method of claim 1, wherein an input ofthe reading data correction model further includes a gas composition anda gas calorific value.
 6. An Internet of Things (IoT) system forcorrecting a smart gas flow, wherein the system includes a userplatform, a service platform, a management platform, a sensor networkplatform, and an object platform, wherein the management platform isconfigured to: obtain reading data of a gas meter; determine a firstconfidence level of the reading data based on the reading data; whereinthe reading data includes first reading data and second reading data,the first reading data being historical reading data of the gas meter,and the second reading data being current reading data corresponding toa current time point, and to determine a first confidence level of thereading data based on the reading data, the management platform isfurther configured to: predict, based on the first reading data, adistribution interval of third reading data and a distributionprobability corresponding to the third reading data; the third readingdata being a theoretical value of the current reading data, and thecurrent reading data being a gas consumption from time when the gasmeter starts metering to the current time point; and take a distributionprobability corresponding to the second reading data in the distributioninterval of the third reading data as the first confidence level; inresponse to a determination that the first confidence level is smallerthan a confidence level threshold, obtain a working condition parameter;the confidence level threshold being determined through an experiencevalue; and determine, based on the working condition parameter, a gasmeter correction manner; the working condition parameter including astandard temperature and pressure and a current temperature andpressure, and to determine, based on the working condition parameter, agas meter correction manner, the management platform is furtherconfigured to: determine, based on second reading data, the standardtemperature and pressure, and the current temperature and pressure, acorrection value of the second reading data through a reading datacorrection model, wherein the reading data correction model is a machinelearning model.
 7. A non-transitory computer-readable storage mediumstoring computer instructions, wherein when reading the computerinstructions in the storage medium, a computer implements the method ofclaim 1.